My most recent example of this is mentoring young, ambitious, but inexperienced interns. Not only did they produce about the same amount of code in a day that they used to produce in a week (or two), several other things made my work harder than before: - During review, they hadn't thought as deeply about their code so my comments seemed to often go over their heads. Instead of a discussion I'd get something like "go…
Well said. That has been my experience as well, but from the perspective of using these tools on my own. Sure, I can now generate thousands of lines of code relatively quickly, but the hard part is actually reviewing the code to ensure that it does what I asked, fix bugs, hunt for security issues, refactor, simplify and remove code, and so on. I've found that it's often much more productive to write the code myself,…
I think that's probably true, but I think there are multiple layers here.
There's what's commonly called vibe coding, where you don't even look at the code.
Then there's what I'd call augmented coding, where you generate a good chunk of the code, but still refactor and generally try to understand it.
And then there's understanding every line of it. For this, in particular, I don't believe LLMs speed things up. You can get the LLM to _explain_ every line to you, but what I mean is to look at documentation and specs to build your understanding and test out fine grained changes to confirm it. This is something you naturally do while writing code, and unless you type comically slow, I'm not convinced it's not faster this way around. There's a very tight feedback loop when you are writing and testing code atomically. In my experience, this prevents an unreasonable amount of emergencies and makes debugging orders of magnitude faster.
I'd say the bulk of my work is either in the second or the third bucket, depending on whether it's production code, the risks involved etc.
These categories have existed before LLMs. Maybe the first two are cheaper now, but I've seen a lot of code bases that fall into them - copy pasting from examples and SO. That is, ultimately, what LLMs speed up. And I think it's OK for some software to fall into these categories. Maybe we'll see too much fall into them for a while. I think eventually, the incredibly long feedback cycles of business decisions will bite and correct this. If our industry really flies off the handle, we tend to have a nice software crisis and sort it out.
I'm optimistic that, whatever we land on eventually, generative AI will have reasonable applications in software development. I personally already see some.